Apparatuses and a method for artifact reduction in medical images using a neural network

ABSTRACT

A method and apparatuses are provided that use a neural network to correct artifacts in computed tomography (CT) images, especially cone-beam CT (CBCT) artifacts. The neural network is trained using a training dataset of artifact-minimized images paired with respective artifact-exhibiting images. In some embodiments, the artifact-minimized images are acquired using a small cone angle for the X-ray beam, and the artifact-exhibiting images are acquired either by forwarding projecting the artifact-minimized images using a large-cone-angle CBCT configuration or by performing a CBCT scan. In some embodiments, the network is a 2D convolutional neural network, and an artifact-exhibiting image is applied to the neural network as 2D slices taken for the coronal and/or sagittal views. Then the 2D image results from the neural network are reassembled as a 3D imaging having reduced imaging artifacts.

TECHNICAL FIELD

This disclosure relates to using neural networks to reduce artifacts inreconstructed medical images.

BACKGROUND

The background description provided herein is for the purpose ofgenerally presenting the context of the disclosure. Work of thepresently named inventors, to the extent the work is described in thisbackground section, as well as aspects of the description that may nototherwise qualify as prior art at the time of filing, are neitherexpressly nor impliedly admitted as prior art against the presentdisclosure.

Computed tomography (CT) systems and methods are widely used,particularly for medical imaging and diagnosis. CT systems generallycreate images of one or more sectional slices (also referred to assections) through a subject's body. A radiation source, such as an X-raysource, irradiates the body from one side. At least one detector on theopposite side of the body receives radiation transmitted through thebody. The attenuation of the radiation that has passed through the bodyis measured by processing electrical signals received from the detector.

A CT sinogram indicates attenuation through the body as a function ofposition along a detector array and as a function of the projectionangle between the X-ray source and the detector array for variousprojection measurements. In a sinogram, the spatial dimensions refer tothe position along the array of X-ray detectors. The time/angledimension refers to the projection angle of X-rays, which changes as afunction of time during a CT scan. The attenuation resulting from aportion of the imaged object (e.g., a vertebra) will trace out a sinewave around the vertical axis. Performing an inverse Radon transform—orany other image reconstruction method—reconstructs an image from theprojection data in the sinogram. X-ray CT has found extensive clinicalapplications in cancer, heart, and brain imaging. In some X-ray CT scans(e.g., cone-beam CT using a large cone angle), the reconstructed imageshave imaging artifacts. These artifacts can degrade the image qualityand impede clinical applications of the reconstructed images.Accordingly, better method for reducing the imaging artifacts aredesired.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of this disclosure is provided byreference to the following detailed description when considered inconnection with the accompanying drawings, wherein:

FIG. 1A shows an exemplary method 110 to form a training dataset and totrain a neural network, according to an embodiment of the disclosure;

FIG. 1B shows an exemplary method 150 to reduce imaging artifacts usinga trained neural network, according to an embodiment of the disclosure;

FIG. 2A shows an example of a feedforward neural network, according toan embodiment of the disclosure;

FIG. 2B shows an example of a convolution neural network (CNN),according to an embodiment of the disclosure;

FIG. 2C shows an example of implementing a convolution layer, accordingto an embodiment of the disclosure;

FIG. 2D shows an example of a method to train a neural network,according to an embodiment of the disclosure;

FIG. 3 shows an example of a method to generate data in a trainingdataset, according to an embodiment of the disclosure;

FIG. 4A shows an example of a coronal view of a reconstructed image froma cone-beam CT (CBCT) scan that exhibits a cone-beam artifact, accordingto an embodiment of the disclosure;

FIG. 4B shows an example of an axial view of the reconstructed imagefrom the CBCT scan, according to an embodiment of the disclosure;

FIG. 4C shows an example of a coronal view of a reconstructed image froma helical CT scan that does not exhibit the cone-beam artifact,according to an embodiment of the disclosure;

FIG. 4D shows an example of an axial view of the reconstructed imagefrom the helical CT scan, according to an embodiment of the disclosure;

FIG. 5 shows an example of pixels in a large image down-sampled intofour small sub-images, according to an embodiment of the disclosure; and

FIG. 6 shows a schematic of an implementation of a CT scanner, accordingto an embodiment of the disclosure.

DETAILED DESCRIPTION

The apparatuses and methods described herein achieve several advantagesover related methods. These advantages include: reducing computationaltime and hardware costs, and improving image quality of medical images,such as images generated by X-ray computed tomography. Further, theexamples provided herein of applying these methods are non-limiting, andthe methods described herein can benefit other medical imagingmodalities such as single-photon emission computed tomography (SPECT),and the like, by adapting the framework proposed herein. Accordingly,the apparatuses and methods herein described herein are provided asnon-limiting example implementations of the present disclosure. As willbe understood by those skilled in the art, the present disclosure may beembodied in other specific forms without departing from the spirit oressential characteristics thereof. Accordingly, the detailed descriptionis intended to be illustrative, but not limiting of the scope of thedisclosure. The disclosure, including any readily discernible variantsof the teachings herein, defines, in part, the scope of the foregoingclaim terminology such that no inventive subject matter is dedicated tothe public.

In some embodiments of CT, such as volumetric CT, to reduce imaging timeand an X-ray dose, a relatively thick section (or a volume) of an objectis scanned (i.e., imaged) in a single rotation of a CT source and withrespect to the object OBJ being imaged. In some examples, volumetric CT,such as circular cone-beam CT (CBCT), is implemented using an X-ray beamthat has a relatively larger cone angle (e.g., a cone angle that isgreater than a predefined angle threshold) and scans a volume of anobject in one scan. Accordingly, a three-dimensional (3D) image thatshows internal features of the volume being imaged is reconstructedbased on a signal from the detector. The signal corresponding to thedetected radiation of the X-ray beam after having traversed the objectOBJ being imaged is referred to as “projection data.” The reconstructed3D image reconstructed from a CBCT scan can be susceptible to cone-beamartifacts, i.e., undesirable characteristics that degrade image qualitydue to a large cone angle of the X-ray beam. The cone-beam artifacts canbe mitigated using a slower scanning method such as a helical scan witha small cone angle. Other imaging artifacts besides cone-beam artifactscan also result from various aspects of the san and reconstructionprocesses.

To mitigate imaging artifacts, an artificial neural network (oftensimplified as “neural network”), such as a deep neural network, aconvolutional neural network (CNN), and the like, can be trained usingpairs of images including an input image that exhibits the imagingartifacts and a target image that does not exhibit the imagingartifacts. The network is trained such that applying the input image tothe neural network produces a result approximately matching the targetimage. Then the trained neural network can be used to reduce imagingartifacts associated with volumetric CT scans, such as cone-beamartifacts.

That is, the neural network is trained using a training datasetincluding artifact-exhibiting data and artifact-minimized data that hasless imaging artifacts than the artifact-exhibiting data. In someembodiments, artifact-exhibiting data is obtained from correspondingartifact-minimized data using simulation (e.g., forward projecting theartifact-minimized data using a large cone angle configuration togenerate the artifact-exhibiting data).

Referring now to the drawings, where like reference numerals designateidentical or corresponding parts throughout the several views, FIG. 1Ashows a method 110 to form a training dataset and to train a neuralnetwork according to an embodiment of the disclosure. The method 110starts at S111, and proceeds to S112.

At S112, the training dataset having imaging artifacts associated withvolumetric CT scans is obtained. In general, imaging artifacts refer toundesirable characteristics that degrade image quality. Imagingartifacts can be reduced using image processing. In some embodiments,imaging artifacts, such as cone-beam artifacts, occur due to a largecone angle of an X-ray beam used in a CBCT scan. A large cone anglerefers to a cone angle that is greater than a predefined anglethreshold, and a small cone angle refers to a cone angle that is lessthan or equal to the predefined angle threshold. For example, thepredefined angle threshold can be determined empirically based onobservations regarding how large the cone angles can become for aparticular anatomical or diagnostic application before it becomes ahindrance to clinical practice. Cone-beam artifacts often arise becausefor some views of the CT scan the X-ray beam does not pass throughcertain volume pixels (also referred to as voxels) in the reconstructedimage, resulting in insufficient data/sampling for these volume pixelsto be properly reconstructed. Consequently, cone-beam artifacts, such aslow-frequency shading artifacts, streaking cone-beam artifacts, and thelike, can be observed in these under-sampled regions.

Often a 3D reconstructed image is viewed using two-dimensional slices inone of the axial, sagittal, or coronal planes. That is, 2D images can beused to illustrate different cross-sections (or views) of internalfeatures of an object. For example, 2D images can have coronal views,sagittal views, axial views, and the like. In some embodiments, axialviews are perpendicular to the axis of rotation, and coronal views andsagittal views are parallel to the axis of rotation. In some examples,imaging artifacts vary with different views. For example, cone-beamartifacts can be more pronounced in certain views, such as coronal andsagittal views, than other views, such as an axial view, as shown inFIGS. 4A-4D. In some examples, a training dataset includes 2D imageshaving coronal and sagittal views, 3D images, and the like.

In general, a training dataset includes artifact-exhibiting data andartifact-minimized data. In an example, artifact-exhibiting data haveimaging artifacts above a certain threshold, and artifact-minimized datahave imaging artifacts below the certain threshold. In some examples,artifact-exhibiting data is a reconstructed CT image referred to as anartifact-exhibiting image. In some examples, artifact-minimized data isa reconstructed CT image referred to as an artifact-minimized image. Insome examples, artifact-exhibiting data and correspondingartifact-minimized data form a data pair, and imaging artifacts are morepronounced in the artifact-exhibiting data than in the respectiveartifact-minimized data.

In various embodiments, a training dataset can also be tailored todifferent CT scanning methods, protocols, applications, conditions, andthe like, for example, to train various neural networks in reducingimaging artifacts associated with respective CT scanning methods,protocols, applications, conditions, and the like. As a result, thetrained neural networks can be customized and tailored to certain CTscanning methods, protocols, applications, conditions, and the like. Forexample, a trained neural network can be tailored to reduce imagingartifacts associated with certain anatomical structures or region of abody being imaged. Further, a trained neural network is tailored toreduce cone-beam artifacts associated with circular CBCT scans that usean X-ray beam having the large cone angle.

Artifacts-exhibiting data and artifact-minimized data can be generatedusing any suitable methods. In some embodiments, artifact-exhibitingdata and artifact-minimized data are obtained by scanning objects underdifferent scanning conditions, protocols, and the like. In someembodiments, artifact-minimized data can be generated using a process,such as a scanning method, a protocol, and a suitable condition, thatmaintains imaging artifacts below a certain threshold, for example,using optimized scanning conditions, having a high X-ray dose, a smallcone angle X-ray beam, and the like. On the other hand,artifact-exhibiting data can be obtained using scanning conditionsexhibiting relatively large artifacts, such as an X-ray beam having alarge cone angle, and the like.

In certain implementations, the artifact-exhibiting data is obtained byscanning an object using a circular CBCT scan with an X-ray beam havingthe large cone angle, and the corresponding artifact-minimized data areobtained by scanning the same object using a helical CT scan with anX-ray beam having the small cone angle. Accordingly, the images obtainedusing helical CT scans can have image artifacts below a certainthreshold, making them effective as artifact-minimized data.

Additional image processing methods can be included in S112 to reduce atraining time in S114. For examples, certain imaging artifacts, such ascone-beam artifacts, have low-frequency components. According to aspectsof the disclosure, an image where imaging artifacts vary slowly withrespect to space can be down-sampled to obtain multiple sub-images, asdescribed in FIG. 5. The sub-images can be used to form respective datapairs.

In some embodiments, artifact-exhibiting data, such as a 3D image havingcone-beam artifacts, is obtained from corresponding artifact-minimizeddata using simulation, image processing, and the like. Alternatively,artifact-minimized data can also be obtained from correspondingartifact-exhibiting data using simulation, image processing, and thelike.

In some examples, a neuronal network is trained using a trainingdataset, validated using a validation dataset, and further tested usinga test dataset. Therefore, in some embodiments, additional datasets,such as a validation dataset and a test dataset, are formed fromadditional artifact-exhibiting data and artifact-minimized data.

At S114, the neural network is trained based on the training datasetobtained at S112. In some embodiments, the neural network is trainedoffline, and then stored in memory to be used later when a new CT scanis performed and artifact reduction is desired.

In general, a neural network can learn and perform a task from examples,such as a training dataset including artifact-exhibiting data andartifact-minimized data, without task specific instructions. A neuralnetwork can be based on a computational model including nodes. Thenodes, also referred to as neurons, interconnected by connections, canperform computational tasks. In an embodiment, a neural network can becharacterized by a computational model and parameters. In an example,the parameters can include weights and thresholds associated withconnections and nodes in the neural network.

In an embodiment, a neural network can be organized in multiple layerswhere different layers can perform different kinds of computations. Themultiple layers can include an input layer having input nodes, an outputlayer having output nodes, and hidden layers between the input layer andthe output layer. In an embodiment, the input layer can receive an inputsignal originated from outside of the neural network. In an example, theinput signal is artifact-exhibiting data such as an image havingcone-beam artifacts. The output layer can send a result to outside ofthe neural network. In an example, the result is an image having reducedcone-beam artifacts. In some embodiments, a neural network can be a deepneural network that has, for example, a relatively larger number ofhidden layers than that of a shallow neural network. In an example, aneural network can be a CNN.

In various embodiments, a computational model of a neural network can bedetermined by search algorithms, and the like. Subsequently, the neuralnetwork can be trained using examples related to a certain task, such asreducing image artifacts. As a result, the parameters are modifiedrepetitively when additional examples are used. In an embodiment, alarge number of examples can be organized into multiple independentdatasets, such as a training dataset and a validation dataset, to trainand validate a neural network to obtain an optimal neural network.

In an embodiment, neural networks having various computational modelscan be trained using multiple training methods based on a trainingdataset including data pairs. A data pair includes having an inputsignal, such as artifact-exhibiting data, and an expected output signal,such as artifact-minimized data. An input layer of a neural network canreceive the input signal, and the neural network can subsequentlygenerate a result via the output layer. The result can be compared withthe expected output signal. The parameters of the neural network aremodified or optimized to minimize a difference between the result andthe expected output signal.

In some embodiments, the neural network is trained using the trainingdataset obtained in S112 to optimize parameters of the neural network.Neural networks can be trained using respective training methods to haveoptimized parameters. An optimal neural network is obtained by furtherapplying a validation dataset on the trained neural networks, analyzingthe results and the expected output signals associated with thevalidation dataset. The optimal neural network can then be deployed toperform a certain task. In addition, performance of the optimal neuralnetwork can be further assessed by a test dataset. In an example, thetest dataset is independent from other datasets, such as the trainingdataset and the validation dataset.

In some embodiments, the neural network can be repetitively trained whenadditional artifact-exhibiting data and artifact-minimized data areavailable. For example, steps S112 and S114 can be implementedrepetitively.

In some embodiments, the neural network is trained to reduce imagingartifacts, such as cone-beam artifacts associated with volumetric CTscans including CBCT scans, when the training dataset includesartifact-exhibiting images and artifact-minimized images. The method 110then proceeds to S119, and terminates.

FIG. 1B shows a method 150 according to an embodiment of the disclosure.In some embodiments, the method 150 is used to reduce image artifacts,such as cone-beam artifacts, of an input image by using a suitableneural network, such as the neural network trained using the method 110.The method 150 starts at S151, and proceeds to S152.

At S152, input data to the neural network is obtained. In an embodiment,the input data is an input image, a reconstructed CT image havingimaging artifacts associated with volumetric CT scans, such as cone-beamartifacts. In some examples, the input image is a 3D image, for example,reconstructed from corresponding projection data obtained using acircular CBCT scan, a 2D image, such as a coronal view or a sagittalview of a respective 3D image having imaging artifacts, and the like.

In some embodiments, imaging artifacts of an original image have certainfeatures. The original image can be processed based on the features togenerate the input data to the neural network to make the method 150more efficient. For example, the imaging artifacts vary slowly withrespect to space, i.e., the imaging artifacts are dominated by lowspatial frequency components. Therefore, the original image can beFourier transformed into a spatial frequency domain and includes alow-frequency component and a high-frequency component. Further, theoriginal image can be low pass filtered in the spatial frequency domainto obtain the low-frequency component. The low-frequency component isselected to be the input data to the neural network at S152. Further,the low-frequency component can be downsampled to decrease the size ofthe input data that is applied to the neural network at S152, improvingthe computational efficiency of training and then using the neuralnetwork at S152. That is, when input data is smaller (i.e., has fewerpixels), applying the input data to the neural network at S152 can beperformed using fewer computations.

In certain implementations, other methods of obtaining the low-frequencycomponent can be used without departing from the spirit of the methodsdescribed herein, as would be understood by a person of ordinary skillin the art. For example, the low-frequency component can be generated byaveraging (e.g., averaging N-by-M blocks) or by downsampling by factorsof N and M in respective directions of the original image. In certainimplementations N and M can be equal (e.g., in FIG. 5 N=M=2). Once, thelow-frequency component is generated the high-frequency component can begenerated in various way, as would be understood by a person of ordinaryskill in the art, including, e.g., by subtracting the low-frequencycomponent from the original image. Thus, all of the information in theoriginal image can be preserved in the combination of the low-frequencycomponent together with the high-frequency component. When the artifactsare predominantly in the low-frequency component (e.g., in cone-beamartifacts), applying the low-frequency component, which is downsampled(e.g., by a factor N in a first direction and M in a second directions),to the neural network the artifact can be efficiently mitigated, andthen the resolution of the original image can be restored by combiningthe result of the neural network 152 with the high-frequency component,which has not been applied to the neural network 152.

At S154, a suitable neural network is determined to process the inputdata, such as an input image. In some examples, a neural network trainedto reduce imaging artifacts associated with volumetric CT scans, such ascone-beam artifacts, is selected to process the input data. In someexamples, as described above, neural networks can be customized andtailored to certain CT scanning methods, protocols, applications,conditions, and the like by using respective training datasets.Therefore, in some examples, the neural network is determined based oncharacteristics of the input data. In an example, a neural networktrained with 3D images is selected to reduce imaging artifacts of theinput data, such as a 3D image. In an example, a neural network trainedwith images having imaging artifacts that vary slowly with respect tospace is selected to reduce imaging artifacts of the input data havingsimilar property.

At S156, the input data is processed using the determined neural networkand output data having reduced artifacts is generated.

At S158, an output image is obtained based on the output data. Invarious embodiments, the output data, such as a 2D or 3D image, can befurther processed by suitable image processing methods to generate theoutput image. In some examples, as described in step S152, the originalimage is low pass filtered in the spatial frequency domain to obtain thelow-frequency component that is processed by the neural network.Accordingly, at S158, the output data is combined with the correspondinghigh-frequency component of the original image to form the output image.

In some examples, step S158 is omitted, and the output image is theoutput data. The method then proceeds to S159, and terminates.

FIG. 2A shows an exemplary feedforward neural network 201 according toan embodiment of the disclosure. For example, the neural network 201 hasN inputs, K hidden layers, and three outputs. Each layer is made up ofnodes, and each node performs a weighted sum of the inputs and comparesa result of the weighted sum to a threshold to generate an output (alsoreferred to as a result). Neural networks make up a class of functionsfor which the members of the class are obtained by varying thresholds,connection weights, or specifics of the architecture such as the numberof nodes and/or their connectivity. In an example, a relatively simpleneural network, such as an autoencoder, has three layers. A deep neuralnetwork generally has more than three layers of neurons, and has as manyoutputs neurons as input neurons, where N, for example, is a number ofpixels in an reconstructed image. In some examples, the connectionsbetween neurons store values called “weights” (also interchangeablyreferred to as “coefficients” or “weighting coefficients”) thatmanipulate data in the calculations. The outputs of the neural networkdepend on three types of parameters: (i) the interconnection patternbetween the different layers of neurons, (ii) the learning process forupdating the weights of the interconnections, and (iii) the activationfunction that converts a neuron's weighted input to the outputactivation.

Mathematically, a neuron's network function m(x) is defined as acomposition of other functions n_(i)(x), which can further be defined asa composition of other functions. This can be conveniently representedas a network structure, with arrows depicting the dependencies betweenvariables, as shown in FIGS. 2A-2C. For example, the neural network canuse a nonlinear weighted sum, m(x)=K(Σ_(i)w_(i)n_(i)(x)), where K(commonly referred to as the activation function) is certain predefinedfunction, such as the hyperbolic tangent.

In FIG. 2A (and similarly in FIG. 2B), the neurons are depicted bycircles around a threshold function or circles. For the non-limitingexample shown in FIG. 2A, the inputs are depicted as circles around alinear function, and the arrows indicate directed connections betweenneurons. In certain implementations, the neural network trained in S114is a feedforward network as exemplified in FIGS. 2A and 2B (e.g., it canbe represented as a directed acyclic graph).

The neural network operates to achieve a specific task, such as reducingimaging artifacts, by searching within the class of functions F tolearn, using a set of observations, to find m*∈F which solves thespecific task in certain optimal sense (e.g., the stopping criteria usedin step S260 in the method 200 described below). For example, in certainimplementations, this can be achieved by defining a cost function C: F→

such that, for the optimal solution m*, C(m*)≤C(m)∀m∈F (i.e., nosolution has a cost less than the cost of the optimal solution).

refers to the set of real numbers. The cost function C is a measure ofhow far away a particular solution is from an optimal solution to theproblem to be solved (e.g., the error). Learning algorithms iterativelysearch through the solution space to find a function that has thesmallest possible cost. In certain implementations, the cost isminimized over a sample of the data (i.e., the training dataset).

FIG. 2B shows an exemplary CNN 202 according to an embodiment of thedisclosure. In some embodiments, CNNs have beneficial properties forimage processing, thus, are relevant for applications of reducingimaging artifacts. In various embodiments, CNNs use feed-forward neuralnetworks where the connectivity pattern between neurons can representconvolutions in image processing. For example, CNNs can be used forimage processing optimization by using multiple layers of small neuroncollections which process portions of an input image, called “receptivefields”, also referred to as “perception fields.” The outputs of thesecollections can then tiled so that they overlap, to obtain a betterrepresentation of the original image. This processing pattern can berepeated over multiple layers having alternating convolution and poolinglayers.

FIG. 2C shows an example 203 of implementing a convolution layeraccording to an embodiment of the disclosure. Referring to FIG. 2C, a4×4 kernel 204 is applied to map values from an input layer representinga 2D image to a first hidden layer, which is a convolution layer. Thekernel maps respective 4×4 pixel regions 204 to corresponding neurons205 of the first hidden layer. In the non-limiting example illustratedin FIG. 2C, for example, the receptive/perception field is 4 pixels by 4pixels.

Following a convolutional layer, a CNN can include local and/or globalpooling layers, which combine the outputs of neuron clusters in theconvolution layers. Additionally, in certain implementations, the CNNcan also include various combinations of convolutional and fullyconnected layers, with pointwise nonlinearity applied at the end of orafter each layer.

CNNs have several advantages for image processing. To reduce the numberof free parameters and improve generalization, a convolution operationon small regions of input is introduced. One significant advantage ofcertain implementations of CNNs is the use of shared weight inconvolutional layers. Therefore, the same filter (weights bank) is usedas the coefficients for each pixel in the layer, thus, reducing memoryfootprint and improving performance. Compared to other image-processingmethods, CNNs advantageously use relatively little pre-processing. Thismeans that the neural network is responsible for learning the filtersthat in traditional algorithms were hand-engineered. The lack ofdependence on prior knowledge and human effort in designing features isa major advantage for CNNs.

In certain implementations, the neural network trained in S114 includesmultiple neural networks that are suitably connected to perform a task,such as reducing imaging artifacts.

FIG. 2D shows a method 200 according to an embodiment of the disclosure.In some embodiments, the method 200 is an implementation of S114 of themethod 110 for training the neural network using the training datasetobtained in S112. In an example, FIG. 2D shows one implementation ofsupervised learning used to train the neural network in S114 accordingto an embodiment of the disclosure. In supervised learning, a trainingdataset including artifact-exhibiting data and artifact-minimized datais obtained, and the neural network is iteratively updated to reduce theerror, such that a result from the neural network based on theartifact-exhibiting data closely matches the artifact-minimized data. Inother words, the neural network infers the mapping implied by thetraining dataset, and the cost function produces an error value relatedto the mismatch between the artifact-minimized data and the resultproduced by applying a current incarnation of the neural network to theartifact-exhibiting data. For example, in certain implementations, thecost function can use the mean-squared error to minimize the averagesquared error. In the case of a of multilayer perceptrons (MLP) neuralnetwork, the backpropagation algorithm can be used for training theneural network by minimizing the mean-squared-error-based cost functionusing a gradient descent method.

In some embodiments, training a neural network model refers to selectingone model from a set of allowed models (or, in a Bayesian framework,determining a distribution over the set of allowed models) thatminimizes the cost criterion (i.e., the error value calculated using thecost function). Generally, the neural network can be trained using anyof numerous algorithms for training neural network models (e.g., byapplying optimization theory and statistical estimation).

For example, the optimization method used in training neural networkscan use some form of gradient descent, using backpropagation to computethe actual gradients. This is done by taking the derivative of the costfunction with respect to the network parameters and then changing thoseparameters in a gradient-related direction. The backpropagation trainingalgorithm can be: a steepest descent method (e.g., with variablelearning rate, with variable learning rate and momentum, and resilientbackpropagation), a quasi-Newton method (e.g.,Broyden-Fletcher-Goldfarb-Shannon, one step secant, andLevenberg-Marquardt), or a conjugate gradient method (e.g.,Fletcher-Reeves update, Polak-Ribiére update, Powell-Beale restart, andscaled conjugate gradient). Additionally, evolutionary methods, such asgene expression programming, simulated annealing,expectation-maximization, non-parametric methods and particle swarmoptimization, can also be used for training the neural networks.

Referring to FIG. 2D, the method 200 starts at S211, and proceeds toS210. The training dataset can include an image exhibiting imagingartifacts. For example, an imaging artifact can arise from a certainmethod of reconstruction, or arise from a method used for acquiringprojection data (e.g., a large-angle cone-beam scan), and the like. Insome embodiments, the training dataset includes artifact-exhibiting dataand artifact-minimized data.

At S210, a neural network being trained is initialized. In someexamples, an initial guess is generated for coefficients of the neuralnetwork. For example, the initial guess can be based on a prioriknowledge of an object being imaged. Additionally, the initial guess canbe based on a neural network trained on a training dataset related to adifferent CT scan method.

At S220, an error (e.g., a cost function) is calculated between theartifact-minimized data and a result generated from the neural networkbased on the artifact-exhibiting data. The error can be calculated usingany known cost function or distance measure between image (or projectiondata), including the cost functions described above.

At S230, a change in the error as a function of the change in the neuralnetwork can be calculated (e.g., an error gradient), and the change inthe error can be used to select a direction and step size for asubsequent change to the weights/coefficients of the neural network.Calculating the gradient of the error in this manner is consistent withcertain implementations of a gradient descent optimization method. Incertain other implementations, as would be understood by one of ordinaryskill in the art, this step can be omitted and/or substituted withanother step in accordance with another optimization algorithm (e.g., anon-gradient descent optimization algorithm like simulated annealing ora genetic algorithm).

At S240, a new set of coefficients are determined for the neuralnetwork. For example, the weights/coefficients can be updated using thechange calculated in S230, as in a gradient descent optimization methodor an over-relaxation acceleration method.

At S250, a new error value is calculated using the updatedweights/coefficients of the neural network. In various embodiments, anew error value is calculated between the artifact-minimized data and anew result generated by the updated neural network based on theartifact-exhibiting data.

At S260, predefined stopping criteria are used to determine whether thetraining of the neural network is complete. For example, the predefinedstopping criteria can evaluate whether the new error and/or the totalnumber of iterations performed exceed predefined values. For example,the stopping criteria can be satisfied if either the new error fallsbelow a predefined threshold or if a maximum number of iterations isreached. When the stopping criteria is not satisfied, the method 200will continue back to the start of the iterative loop by returning andrepeating S230 using the new weights and coefficients (the iterativeloop includes steps S230, S240, S250, and S260). When the stoppingcriteria are satisfied, the method 200 terminates at S299.

In addition to the implementation for error minimization shown in FIG.2D, the method 200 can use one of many other known minimization methods,including, e.g., local minimization methods, convex optimizationmethods, and global optimization methods.

When the cost function (e.g., the error) has local minima that aredifferent from the global minimum, a robust stochastic optimizationprocess is beneficial to find the global minimum of the cost function.Examples of optimization method for finding a local minimum can be oneof a Nelder-Mead simplex method, a gradient-descent method, a Newton'smethod, a conjugate gradient method, a shooting method, or other knownlocal optimization method. There are also many known methods for findingglobal minima including: genetic algorithms, simulated annealing,exhaustive searches, interval methods, and other conventionaldeterministic, stochastic, heuristic, and metaheuristic methods. In someembodiments, the above methods can be used to optimize the weights andcoefficients of the neural network. Additionally, neural networks can beoptimized using a back-propagation method.

FIG. 3 shows a method 300 according to an embodiment of the disclosure.In some embodiments, the method 300 is used to implement step S112 inthe method 110. For example, the method 300 generates a training datasetincluding a data pair having artifact-exhibiting data andartifact-minimized data. The method 300 starts at S301, and proceeds toS310.

At S310, a first projection data is obtained. The first projection datarepresents radiation data obtained in one or more CT scans. In someembodiments, the first projection data is measured under optimalconditions, such as using helical CT scans having the small cone angle.

At S320, a first image is generated based on the first projection data,for example, by image reconstruction. In some embodiments, the firstimage is artifact-minimized data that has minimal imaging artifacts,such as below the certain threshold. In some examples, the first imageis a 3D reconstructed image. In some examples, the first image is a 2Dreconstructed image. In some examples, the first image is a 2D coronalview or a 2D sagittal view. In some examples, an intermediate 3D imageis reconstructed from the first projection data. Referring to FIGS.4A-4D, the first image is obtained using certain view(s) that show morepronounced imaging artifacts, such as a coronal view, than otherview(s), such as an axial view, of the intermediate 3D image, thus, thefirst image is a 2D image showing the coronal view.

The image reconstruction can be performed using a back-projectionmethod, a filtered back-projection method, a Fourier-transform basedimage reconstruction method, an iterative image reconstruction method(e.g., algebraic reconstruction technique), a matrix inversion imagereconstruction method, a statistical image reconstruction method, andthe like.

At S330, a second projection data is generated from the first image. Insome embodiments, the second projection data is generated using aforward projection method. In various examples, the second projectiondata is obtained using simulation under a simulation condition that canproduce relatively large imaging artifacts, such as a circular CBCT scanconfiguration that uses an X-ray beam having the large cone angle.

In some examples, multiple second projection data are simulated from thesame first image under different simulation conditions that can producerelatively large imaging artifacts. For example, the multiple secondprojection data can be simulated to represent various values of one ormore scanning parameters. These scanning parameters can include, e.g., ascanning protocol, and a diagnostic application. In some cases, thedifferent scanning parameters can correspond to different first images,such as when the scanning parameters are anatomic structure to be imagedor a diagnostic application. In certain implementations, the multiplesecond projection data are obtained using different cone angles that aregreater than the angle threshold. Further, different neural networks canbe optimized for specific values of the one or more scanning parameters.For example, a given neural network can be trained to be used for afirst cone angle and a first anatomical structure to be imaged (e.g., ahead). Another neural network can be trained to be used for the firstcone angle and a second anatomical structure to be imaged (e.g., atorso). A third neural network can be trained to be used for a secondcone angle and the first anatomical structure to be imaged, and soforth. For each neural network, the choice of the scanning parametersfor the first images and the scanning parameters for the simulation ofthe corresponding second images will be chosen based on the designationof the neural network.

At S340, a second image is obtained based on the second projection data,for example, by image reconstruction. In some embodiments, the secondimage is artifact-exhibiting data that has relatively large imagingartifacts, such as above the certain threshold. In some examples, thesecond image has a same dimension, such as 2D, 3D, and the like, as thatof the first image. In some examples, the second image has a same view,such as a coronal view, a sagittal view, and the like, as that of thefirst image. The image reconstruction can be similar or identical to theimage reconstruction described in S320.

At S350, a training dataset is generated. In some embodiments, thetraining dataset includes the first image and the second image that forma data pair. In some embodiments, the training dataset includes thefirst image and multiple second images that correspond to the firstimage. For example, as discussed above, the same first image can beforward projected using different values for the scanning parameters,such as different cone angles, and each of these different values can beused to reconstruct a respective second image. In this case, each of therespective second images would be paired with the first image in arespective training dataset to train a neural network designated by therespective values of the scanning parameters. Further, steps S310, S320,S330, S340, and S350 can be repeated to generate additional data pairs,each including a first image and a second image, and the like to beincluded in the training dataset. In some examples, a validation datasetand a testing dataset are generated using additional first images andrespective second images. The method 300 proceeds to S399, andterminates.

In some embodiments, a neural network can also be trained using 2Dimages, for example, because training a neural network with 2D imagescan be faster. As described above, imaging artifacts vary with differentviews. For example, cone-beam artifacts can be more pronounced incertain views, such as coronal and sagittal views as compared with anaxial view, as shown below in FIGS. 4A-4D. In some examples, suitableviews, such as coronal views and sagittal views that have relativelylarge imaging artifacts are included in the training dataset in S112.

FIGS. 4A-4D show exemplary images 410, 415, 420, and 425 according to anembodiment of the disclosure. FIGS. 4A and 4B show the first coronalview 410 and a second coronal view 415 of a first object, for example, afirst region of a body. In some embodiments, the first coronal view 410is generated under optimal imaging conditions using an X-ray beam havingthe small cone angle and corresponds to artifact-minimized data. Thesecond coronal view 415 is generated using an X-ray beam having thelarge cone angle and corresponds to artifact-exhibiting data. Forexample, areas 411 and 412 illustrate pronounced cone-beam artifactsthat are not detectable in the first coronal view 410.

FIGS. 4C and 4D show a first axial view 420 and a second axial view 425of a second object, for example, a second region of a body. In someembodiments, the first axial view 420 is generated under optimal imagingconditions an X-ray beam having using the small cone angle. The secondaxial view 425 is generated using an X-ray beam having the large coneangle. For example, area 421 illustrates cone-beam shading artifactsthat are not detectable in the first axial view 420. Referring to FIGS.4B and 4D, however, the cone-beam artifacts illustrated by the areas 411and 412 in the coronal view are more pronounced than the cone-beamartifacts illustrated by the area 421 in the axial view. According toembodiments of the disclosure, 2D coronal views, such as the firstcoronal view 410 and the second coronal view 415 corresponding to theartifact-minimized data and the artifact-exhibiting data are included ina training dataset in step S112. In some examples, the first axial view420 and the second axial view 425 are excluded from a training datasetin step S112. Further, in some embodiments, 2D sagittal viewscorresponding to artifact-exhibiting data and artifact-minimized dataare also included in a training dataset in step S112.

Additional image processing methods can be included, for example, in themethods 110 and 300 to reduce training time. Certain imaging artifacts,such as cone-beam artifacts, vary slowly with respect to space, i.e.,have low spatial frequency components. When the neural network trainedin the method 110 is a CNN, a relatively large receptive field is used,for example, to account for the low spatial frequency components.According to aspects of the disclosure, an image is split into multiplesub-images that have a smaller number of pixels while a receptive fieldof each sub-image is comparable to a receptive field of the image. Insome examples, the image is down-sampled to obtain the multiplesub-images, as described below. The respective sub-images are includedin a training dataset, such as the training dataset generated using themethod 110.

FIG. 5 shows a non-limiting example of pixels in an image 550 beingdown-sampled and subdivided into four smaller sub-images 521-524,according to an embodiment of the disclosure. For example, image 550 canbe a 2D slice of a reconstructed image in a sagittal or coronal plane.In FIG. 5, the image 550 is down-sampled by a factor 2 in a firstdirection and a factor 2 in the second direction. In general, image 550can be down-sampled by a factor N in a first direction and a factor M inthe second direction, in which case N×M sub-images would be generated.When the image 550 is to be applied to a CNN (e.g., to train the CNN),the pixels can be grouped into respective 2-by-2 blocks, such as thefirst block 551, and the pixels within each block given an index from 1to 4. All of the pixels having the same index are combined to formrespective sub-images. For example, the pixel 551(1) from the pixelgroup 551 is shown in sub-image 521, and the pixel 551(2) from the pixelgroup 551 is shown in sub-image 522. Further, the pixel 551(3) from thepixel group 551 is shown in sub-image 523, and the pixel 551(4) from thepixel group 551 is shown in sub-image 524. Accordingly, all of theinformation from image 550 is preserved in the sub-images 521-524.

In addition to decreasing the number of pixels per sub-image by a factorof N×M (e.g., in FIG. 5, N=M=2), down-sampling also decreases the numberof pixels per receptive field by a factor of N×M, resulting in a totaldecrease by a factor of N²×M² for the number of multiplications toperform a convolutional layer on a sub-image as opposed to the originalimage. For example, FIG. 5 shows a 6-by-6 receptive field 552 for image550. For sub-images 521-524, however, the corresponding receptive fields552(1)-(4) each have dimensions of 3-by-3 (i.e., 114^(th) the number ofpixels as in image 552). When each sub-image can be applied to a neuralnetwork for down-sampled image, much fewer calculations are required.Further, during training, the neural network can converge more quicklyto the optimal weighting coefficients between layers.

Accordingly, the image 550 is down-sampled into the four sub-images521-524 having a lower image resolution than an image resolution of theimage 550. In various embodiments, image resolution has a unit of mm²per pixel in a 2D image and mm³ per pixel in a 3D image, where 1 mm is 1millimeter. The sub-images 521-524 include the respective pixels indexedby 1, 2, 3, or 4. Referring to FIG. 5, a number of pixels in each secondreceptive field is ¼ of a number of pixels in the first receptive field552, thus, training a CNN with sub-images is faster. Note that thesecond receptive field 552 in the original image 550, although includingfour time more pixels, represents a same physical area as is representedby each of the receptive field 552(1)-(4) in the sub-images 521-524.

FIG. 6 shows a schematic of an implementation of a CT scanner accordingto an embodiment of the disclosure. Referring to FIG. 6, a radiographygantry 500 is illustrated from a side view and further includes an X-raytube 501, an annular frame 502, and a multi-row ortwo-dimensional-array-type X-ray detector 503. The X-ray tube 501 andX-ray detector 503 are diametrically mounted across an object OBJ on theannular frame 502, which is rotatably supported around a rotation axisRA (or an axis of rotation). A rotating unit 507 rotates the annularframe 502 at a high speed, such as 0.4 sec/rotation, while the objectOBJ is being moved along the axis RA into or out of the illustratedpage.

X-ray CT apparatuses include various types of apparatuses, e.g., arotate/rotate-type apparatus in which an X-ray tube and X-ray detectorrotate together around an object to be examined, and astationary/rotate-type apparatus in which many detection elements arearrayed in the form of a ring or plane, and only an X-ray tube rotatesaround an object to be examined. The present disclosure can be appliedto either type. The rotate/rotate type will be used as an example forpurposes of clarity.

The multi-slice X-ray CT apparatus further includes a high voltagegenerator 509 that generates a tube voltage applied to the X-ray tube501 through a slip ring 508 so that the X-ray tube 501 generates X-rays.The X-rays are emitted towards the object OBJ, whose cross sectionalarea is represented by a circle. For example, the X-ray tube 501 havingan average X-ray energy during a first scan that is less than an averageX-ray energy during a second scan. Thus, two or more scans can beobtained corresponding to different X-ray energies. The X-ray detector503 is located at an opposite side from the X-ray tube 501 across theobject OBJ for detecting the emitted X-rays that have transmittedthrough the object OBJ. The X-ray detector 503 further includesindividual detector elements or units.

The CT apparatus further includes other devices for processing thedetected signals from X-ray detector 503. A data acquisition circuit ora Data Acquisition System (DAS) 504 converts a signal output from theX-ray detector 503 for each channel into a voltage signal, amplifies thesignal, and further converts the signal into a digital signal. The X-raydetector 503 and the DAS 504 are configured to handle a predeterminedtotal number of projections per rotation (TPPR).

The above-described data is sent to a preprocessing device 506, which ishoused in a console outside the radiography gantry 500 through anon-contact data transmitter 505. The preprocessing device 506 performscertain corrections, such as sensitivity correction on the raw data. Amemory 512 stores the resultant data, which is also called projectiondata at a stage immediately before reconstruction processing. The memory512 is connected to a system controller 510 through a data/control bus511, together with a reconstruction device 514, input device 515, anddisplay 516. The system controller 510 controls a current regulator 513that limits the current to a level sufficient for driving the CT system.

The detectors are rotated and/or fixed with respect to the patient amongvarious generations of the CT scanner systems. In one implementation,the above-described CT system can be an example of a combinedthird-generation geometry and fourth-generation geometry system. In thethird-generation system, the X-ray tube 501 and the X-ray detector 503are diametrically mounted on the annular frame 502 and are rotatedaround the object OBJ as the annular frame 502 is rotated about therotation axis RA. In the fourth-generation geometry system, thedetectors are fixedly placed around the patient and an X-ray tuberotates around the patient. In an alternative embodiment, theradiography gantry 500 has multiple detectors arranged on the annularframe 502, which is supported by a C-arm and a stand.

The memory 512 can store the measurement value representative of theirradiance of the X-rays at the X-ray detector unit 503. Further, thememory 512 can store a dedicated program for executing, for example,various steps of the methods 110, 150, 200, and 300 for training aneural network and reducing imaging artifacts.

The reconstruction device 514 can execute various steps of the methods110, 150, 200, and 300. Further, reconstruction device 514 can executepre-reconstruction processing image processing such as volume renderingprocessing and image difference processing as needed.

The pre-reconstruction processing of the projection data performed bythe preprocessing device 506 can include correcting for detectorcalibrations, detector nonlinearities, and polar effects, for example.

Post-reconstruction processing performed by the reconstruction device514 can include filtering and smoothing the image, volume renderingprocessing, and image difference processing as needed. The imagereconstruction process can implement various of the steps of methods110, 150, 200, and 300 in addition to various CT image reconstructionmethods. The reconstruction device 514 can use the memory to store,e.g., projection data, reconstructed images, calibration data andparameters, and computer programs.

The reconstruction device 514 can include a CPU (processing circuitry)that can be implemented as discrete logic gates, as an ApplicationSpecific Integrated Circuit (ASIC), a Field Programmable Gate Array(FPGA) or other Complex Programmable Logic Device (CPLD). An FPGA orCPLD implementation may be coded in VHDL, Verilog, or any other hardwaredescription language and the code may be stored in an electronic memorydirectly within the FPGA or CPLD, or as a separate electronic memory.Further, the memory 512 can be non-volatile, such as ROM, EPROM, EEPROMor FLASH memory. The memory 512 can also be volatile, such as static ordynamic RAM, and a processor, such as a microcontroller ormicroprocessor, can be provided to manage the electronic memory as wellas the interaction between the FPGA or CPLD and the memory.

Alternatively, the CPU in the reconstruction device 514 can execute acomputer program including a set of computer-readable instructions thatperform the functions described herein, the program being stored in anyof the above-described non-transitory electronic memories and/or a harddisk drive, CD, DVD, FLASH drive or any other known storage media.Further, the computer-readable instructions may be provided as a utilityapplication, background daemon, or component of an operating system, orcombination thereof, executing in conjunction with a processor, such asa Xenon processor from Intel of America or an Opteron processor from AMDof America and an operating system, such as Microsoft VISTA, UNIX,Solaris, LINUX, Apple, MAC-OS and other operating systems known to thoseskilled in the art. Further, CPU can be implemented as multipleprocessors cooperatively working in parallel to perform theinstructions.

In one implementation, the reconstructed images can be displayed on adisplay 516. The display 516 can be an LCD display, CRT display, plasmadisplay, OLED, LED or any other display known in the art.

The memory 512 can be a hard disk drive, CD-ROM drive, DVD drive, FLASHdrive, RAM, ROM or any other electronic storage known in the art.

While certain implementations have been described, these implementationshave been presented by way of example only, and are not intended tolimit the teachings of this disclosure. Indeed, the novel methods,apparatuses and systems described herein may be embodied in a variety ofother forms; furthermore, various omissions, substitutions and changesin the form of the methods, apparatuses and systems described herein maybe made without departing from the spirit of this disclosure.

1. An apparatus, comprising: processing circuitry configured to train aneural network by: obtaining a pair of reconstructed computed tomography(CT) images including an artifact-exhibiting image and anartifact-minimized image, the artifact-exhibiting image having a greaterdegree of artifacts than the artifact-minimized image; forming atraining dataset including the pair of reconstructed CT images; applyingthe artifact-exhibiting image to the neural network to generate anoutput image having reduced artifacts with respect to theartifact-exhibiting image, the neural network including neuronal nodesconnected by connections having weighting coefficients; calculating acost function representing a difference or disagreement between theoutput image and the artifact-minimized data; updating the weightingcoefficients in the neural network to optimize the cost function; andstopping, upon satisfying a predefined stopping criteria, the updatingof the weighting coefficients, and then outputting the neural network asa trained neural network.
 2. The apparatus according to claim 1, whereinthe processing circuitry is further configured to obtain the pair ofreconstructed computed tomography (CT) images by obtaining theartifact-minimized image as an image obtained using an X-ray beam thatspans a solid angle less than a predefined angle threshold, andsimulating, from the artifact-minimized image, the artifact-exhibitingimage, wherein the artifact-exhibiting image is generated fromprojection data corresponding to a cone-beam CT (CBCT) scanconfiguration in which the X-ray beam has a cone angle greater than thepredefined angle threshold.
 3. The apparatus according to claim 2,wherein the processing circuitry is further configured to perform thesimulating of the artifact-exhibiting image by reconstructing theartifact-minimized image from first projection data, the firstprojection data being radiation data obtained using the X-ray beam thatis less than the predefined angle threshold, forward projecting, using aCBCT scan configuration with the X-ray beam that is greater than thepredefined angle threshold, artifact-minimized image to generate secondprojection data, and reconstructing the artifact-exhibiting image fromthe second projection data.
 4. The apparatus according to claim 1,wherein the processing circuitry is further configured to perform theapplying the artifact-exhibiting image to the neural network bypartitioning the artifact-exhibiting image into two-dimensional (2D)slices corresponding to one or more of a coronal view and a sagittalview, applying each of the 2D slices to the neural network, which is a2D convolutional neural network, to generate respective 2D outputimages, and combining the 2D output images to generate the output image,which is a three-dimensional (3D) image.
 5. The apparatus according toclaim 1, wherein the processing circuitry is configured to perform theobtaining the pair of reconstructed CT images by obtaining a pair ofhigh-resolution (HR) reconstructed CT images including an HRartifact-exhibiting image and an HR artifact-minimized image,downsampling the HR artifact-exhibiting image by a factor N in a firstdirection by a factor M in a second direction to generate theartifact-exhibiting image, and downsampling the HR artifacts-minimizedimage by the factor N in the first direction by the factor M in thesecond direction to generate the artifacts-minimized image.
 6. Theapparatus according to claim 5, wherein the processing circuitry isconfigured to upsample the neural network before outputting the neuralnetwork as the trained neural network.
 7. The apparatus according toclaim 1, wherein the processing circuitry is further configured to:acquire the artifacts-minimized image by performing a CT scan using anX-ray beam spanning less than a predefined angle threshold to generatefirst projection data, and reconstructing the artifacts-minimized imagefrom the first projection data, and acquire the artifact-exhibitingimage by performing a cone-beam CT (CBCT) scan using an X-ray beam havecone angle greater than the predefined angle threshold to generatesecond projection data, and reconstructing the artifact-exhibiting imagefrom the second projection data.
 8. The apparatus according to claim 1,wherein the processing circuitry is further configured to: reconstructthe artifacts-minimized image from projection data by performing ahelical CT scan using an X-ray beam spanning less than a predefinedangle threshold, wherein the artifact-exhibiting image represents animage reconstructed from other projection data in which an X-ray beamspanning greater than the predefined angle threshold was used.
 9. Theapparatus according to claim 1, wherein the processing circuitry isconfigured to perform the downsampling the HR artifact-exhibiting andthe HR artifacts-minimized image by one of selecting every Nth voxel inthe first direction and every Mth voxel in the second direction for theHR artifact-exhibiting and the HR artifacts-minimized image,respectively, averaging voxel blocks of size N-by-M of the HRartifact-exhibiting and the HR artifacts-minimized image, respectively,and transforming into a frequency domain and selecting a 1/N percentageof the low-frequency components in the first direction and a 1/Mpercentage of the low-frequency components in the second direction forthe HR artifact-exhibiting and the HR artifacts-minimized image,respectively.
 10. An apparatus, comprising: processing circuitryconfigured to: obtain an artifact-exhibiting image having a computedtomography (CT) imaging artifact, obtain a neural network including aplurality of layers having neuronal nodes connected by connectionshaving weighting coefficients, the plurality of layers including aninput layer receiving an input image and an output layer outputting anoutput image, the neural network being trained to produce the outputimage that has less of the CT imaging artifact than the input image; andapply the artifact-exhibiting image as the input image to the neuralnetwork to generate an artifact-minimized image having less of the CTimaging artifact than the artifact-exhibiting image.
 11. The apparatusaccording to claim 10, wherein the processing circuitry is furtherconfigured to obtain the artifact-exhibiting image, wherein theartifact-exhibiting image is reconstructed from projection data of acone-beam CT (CBCT) scan in which an X-ray beam has a cone angle greaterthan a predefined angle threshold, and the CT imaging artifact includesa cone-beam artifact.
 12. The apparatus according to claim 10, whereinthe processing circuitry is further configured to determine one or morescanning parameters used in a CT scan from which the artifact-exhibitingimage was reconstructed, the one or more scanning parameters being oneor more of an anatomic structure to be imaged, a scanning protocol, anda diagnostic application, and select the neural network from a pluralityof neural networks that are respectively categorized and trainedaccording to the one or more scanning parameters, each of the pluralityof neural networks being trained using a respective training datasetsincluding images having the imaging artifact and being reconstructedfrom CT scans corresponding to a respective scanning parameter of theone or more scanning parameters.
 13. The apparatus according to claim10, wherein the processing circuitry is further configured to apply theartifact-exhibiting image to the neural network by separating theartifact-exhibiting image into a low-frequency image and ahigh-frequency image, the low-frequency image being smaller than theartifact-exhibiting image by a factor N in a first direction and by afactor M in a second direction, applying the low-frequency image to theneural network to generate a low-frequency output image, and combiningthe low-frequency output image with the high-frequency image to generatethe artifact-minimized image.
 14. The apparatus according to claim 10,wherein the processing circuitry is further configured to apply theartifact-exhibiting image to the neural network by partitioning theartifact-exhibiting image, which is a three-dimensional (3D) image, intotwo-dimensional (2D) slices corresponding to one or more of a coronalview and a sagittal view, applying each of the 2D slices to the neuralnetwork, which is a 2D convolutional neural network, to generaterespective 2D output images, and combining the 2D output images togenerate the artifact-minimized image, which is a 3D image.
 15. Theapparatus according to claim 10, further comprising: an X-ray sourceconfigured to radiate X-ray radiation that produces an X-ray beam havinga cone angle that is greater than a predefined angle threshold; adetector including a plurality of detector elements, the detectorconfigured to detect the X-ray radiation from the X-ray source after theX-ray radiation has traversed through an object, and generate projectiondata representing an intensity of the X-ray radiation detected at theplurality of detector elements; and a memory device storing a trainedneural network, which has been trained in advance, as the neuralnetwork.
 16. A method, comprising: obtaining a pair of reconstructedcomputed tomography (CT) images including an artifact-exhibiting imageand an artifact-minimized image, the artifact-exhibiting image having agreater degree of artifacts than the artifact-minimized image; forming atraining dataset including the pair of reconstructed CT images; applyingthe artifact-exhibiting image to the neural network to generate anoutput image having reduced artifacts with respect to theartifact-exhibiting image, the neural network including neuronal nodesconnected by connections having weighting coefficients; calculating acost function representing a difference or disagreement between theoutput image and the artifact-minimized data; updating the weightingcoefficients in the neural network to optimize the cost function; andstopping, upon satisfying a predefined stopping criteria, the updatingthe weighting coefficients, and then outputting the neural network as atrained neural network.
 17. A method, comprising: obtaining anartifact-exhibiting image having a computed tomography (CT) imagingartifact, obtaining a neural network including a plurality of layershaving neuronal nodes connected by connections having weightingcoefficients, the plurality of layers including an input layer receivingan input image and an output layer outputting an output image, theneural network being trained to produce the output image that has lessof the CT imaging artifact than the input image; and applying theartifact-exhibiting image as the input image to the neural network togenerate an artifact-minimized image having less of the CT imagingartifact than the artifact-exhibiting image.
 18. The method according toclaim 17, wherein the applying the artifact-exhibiting image to theneural network further includes separating the artifact-exhibiting imageinto a low-frequency image and a high-frequency image, the low-frequencyimage being smaller than the artifact-exhibiting image by a factor N ina first direction and by a factor M in a second direction, applying thelow-frequency image to the neural network to generate a low-frequencyoutput image, and combining the low-frequency output image with thehigh-frequency image to generate the artifact-minimized image.
 19. Themethod according to claim 16, wherein the obtaining the pair ofreconstructed computed tomography (CT) images further includesreconstructing the artifact-minimized image from first projection data,the first projection data being radiation data obtained using the X-raybeam that is less than a predefined angle threshold, forward projecting,using a CBCT scan configuration with the X-ray beam that is greater thanthe predefined angle threshold, artifact-minimized image to generatesecond projection data, and reconstructing the artifact-exhibiting imagefrom the second projection data.
 20. The method according to claim 15,wherein the applying the artifact-exhibiting image to the neural networkfurther includes partitioning the artifact-exhibiting image intotwo-dimensional (2D) slices corresponding to one or more of a coronalview and a sagittal view, applying each of the 2D slices to the neuralnetwork, which is a 2D convolutional neural network, to generaterespective 2D output images, and
 21. A non-transitory computer readablestorage medium including executable instructions, wherein theinstructions, when executed by circuitry, cause the circuitry to performthe method according to claim 16.